机器学习实战--二分类（MNIST数据集）
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
Scikit-Learn 加载数据集通常具有类似于字典的结构，包括：
DESCR：描述数据集
data:包含一个数组，每个实例为一行，每个特征为一列
target：包含一个带有标记的数组
X,y = mnist["data"], mnist["target"]
# 获得第一个数据
some_digit=X[0]
some_digit
array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 51, 159, 253,
159, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 48, 238,
252, 252, 252, 237, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 54,
227, 253, 252, 239, 233, 252, 57, 6, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,
60, 224, 252, 253, 252, 202, 84, 252, 253, 122, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 163, 252, 252, 252, 253, 252, 252, 96, 189, 253, 167, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 51, 238, 253, 253, 190, 114, 253, 228, 47, 79, 255,
168, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 48, 238, 252, 252, 179, 12, 75, 121, 21, 0,
0, 253, 243, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 38, 165, 253, 233, 208, 84, 0, 0, 0,
0, 0, 0, 253, 252, 165, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 7, 178, 252, 240, 71, 19, 28, 0,
0, 0, 0, 0, 0, 253, 252, 195, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 57, 252, 252, 63, 0, 0,
0, 0, 0, 0, 0, 0, 0, 253, 252, 195, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 198, 253, 190, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 253, 196, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 76, 246, 252,
112, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 253, 252,
148, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85,
252, 230, 25, 0, 0, 0, 0, 0, 0, 0, 0, 7, 135,
253, 186, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 85, 252, 223, 0, 0, 0, 0, 0, 0, 0, 0, 7,
131, 252, 225, 71, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 85, 252, 145, 0, 0, 0, 0, 0, 0, 0,
48, 165, 252, 173, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 86, 253, 225, 0, 0, 0, 0, 0,
0, 114, 238, 253, 162, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 85, 252, 249, 146, 48, 29,
85, 178, 225, 253, 223, 167, 56, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 85, 252, 252, 252,
229, 215, 252, 252, 252, 196, 130, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 199,
252, 252, 253, 252, 252, 233, 145, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 25, 128, 252, 253, 252, 141, 37, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0], dtype=uint8)
# 图片是28*28像素的
some_digit_image=some_digit.reshape(28,28)
some_digit_image
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 51, 159, 253, 159, 50, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 48, 238, 252, 252, 252, 237, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
54, 227, 253, 252, 239, 233, 252, 57, 6, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 60,
224, 252, 253, 252, 202, 84, 252, 253, 122, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 163, 252,
252, 252, 253, 252, 252, 96, 189, 253, 167, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 51, 238, 253,
253, 190, 114, 253, 228, 47, 79, 255, 168, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 48, 238, 252, 252,
179, 12, 75, 121, 21, 0, 0, 253, 243, 50, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 38, 165, 253, 233, 208,
84, 0, 0, 0, 0, 0, 0, 253, 252, 165, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 7, 178, 252, 240, 71, 19,
28, 0, 0, 0, 0, 0, 0, 253, 252, 195, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 57, 252, 252, 63, 0, 0,
0, 0, 0, 0, 0, 0, 0, 253, 252, 195, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 198, 253, 190, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 255, 253, 196, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 76, 246, 252, 112, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 253, 252, 148, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 85, 252, 230, 25, 0, 0, 0,
0, 0, 0, 0, 0, 7, 135, 253, 186, 12, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 85, 252, 223, 0, 0, 0, 0,
0, 0, 0, 0, 7, 131, 252, 225, 71, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 85, 252, 145, 0, 0, 0, 0,
0, 0, 0, 48, 165, 252, 173, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 86, 253, 225, 0, 0, 0, 0,
0, 0, 114, 238, 253, 162, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 85, 252, 249, 146, 48, 29, 85,
178, 225, 253, 223, 167, 56, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 85, 252, 252, 252, 229, 215, 252,
252, 252, 196, 130, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 28, 199, 252, 252, 253, 252, 252,
233, 145, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 25, 128, 252, 253, 252, 141,
37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0]], dtype=uint8)
# cmap 颜色图谱（colormap), 默认绘制为RGB(A)颜色空间。 binary
plt.imshow(some_digit_image,cmap="binary")
# 不显示坐标轴、坐标轴标签，等价于布尔值False
#plt.axis("off")
y[0]
0.0
# from sklearn.model_selection import train_test_split
# Xtrain, Xtest, Ytrain, Ytest=train_test_split(mnist.data,mnist.target,train_size=0.7)
# 数据集划分
Xtrain, Xtest, Ytrain, Ytest=X[:60000],X[60000:],y[:60000],y[60000:]
y_train_5=(Ytrain==5)
y_train_5
array([False, False, False, ..., False, False, False])
y_test_5=(Ytest==5)
y_test_5
array([False, False, False, ..., False, False, False])
# 挑选分类器
from sklearn.linear_model import SGDClassifier # 随机梯度下降分类器 SGD
sgd_clf=SGDClassifier(random_state=42) #SGD训练时完全随机，如果希望得到可复现的结果 需要设置参数random_state
sgd_clf.fit(Xtrain,y_train_5)
sgd_score=sgd_clf.score(Xtest,y_test_5)
sgd_score
0.968
# 用第一张图测试
sgd_clf.predict([some_digit])
array([False])
# 分层抽样
from sklearn.model_selection import StratifiedKFold
from sklearn.base import clone
# n_splits:折叠次数，默认为3，至少为2。
# shuffle:是否在每次分割之前打乱顺序。
# random_state:随机种子，在shuffle==True时使用，默认使用np.random。
skfolds = StratifiedKFold(n_splits=3,random_state=42)
for train_index,test_index in skfolds.split(Xtrain,y_train_5):
print("train_index",train_index)
clone_clf = clone(sgd_clf) # 分类器副本
Xtrain_folds=Xtrain[train_index]
print("Xtrain_folds",Xtrain_folds)
y_tarin_folds=y_train_5[train_index]
X_test_fold=Xtrain[test_index]
y_test_fold=y_train_5[test_index]
clone_clf.fit(Xtrain_folds,y_tarin_folds)
y_pred=clone_clf.predict(X_test_fold)
n_correct=sum(y_pred==y_test_fold)
print(n_correct/len(y_pred))
train_index [18193 18194 18195 ... 59997 59998 59999]
Xtrain_folds [[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
...
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]]
D:\Anaconda3\envs\sklearn\lib\site-packages\sklearn\linear_model\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
"and default tol will be 1e-3." % type(self), FutureWarning)
0.869
train_index [ 0 1 2 ... 59997 59998 59999]
Xtrain_folds [[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
...
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]]
D:\Anaconda3\envs\sklearn\lib\site-packages\sklearn\linear_model\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
"and default tol will be 1e-3." % type(self), FutureWarning)
0.83725
train_index [ 0 1 2 ... 41804 41805 41806]
Xtrain_folds [[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
...
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]]
D:\Anaconda3\envs\sklearn\lib\site-packages\sklearn\linear_model\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
"and default tol will be 1e-3." % type(self), FutureWarning)
0.90535
`
# cross_val_score() K-折交叉验证
from sklearn.model_selection import cross_val_score
cross_val_score(sgd_clf,Xtrain,y_train_5,cv=10,scoring='accuracy')
array([0.9481753 , 0.963 , 0.96433333, 0.9465 , 0.94833333,
0.96633333, 0.88166667, 0.97016667, 0.91466667, 0.96866144])
# 评估分类器性能的最好方法是混淆矩阵
from sklearn.model_selection import cross_val_predict
# cross_val_predict和cross_val_score同样执行K-折交叉验证，但返回的不是评估分数，而是每个折叠的预测。这意味着对于每个实例都可以得到一个干净的预测
# 干净的意思是模型预测是使用的数据在其训练期间从未见过
y_train_pred=cross_val_predict(sgd_clf,Xtrain,y_train_5,cv=3)
y_train_pred
array([False, True, False, ..., False, False, False])
# 获得混淆矩阵
from sklearn.metrics import confusion_matrix
confusion_matrix(y_train_5,y_train_pred)
array([[47700, 6879],
[ 889, 4532]], dtype=int64)
预测
非5 5
非5 47700 6879
5 889 4532
# 精度和召回率
from sklearn.metrics import precision_score,recall_score
# 精度 预测为正的样例中有多少是真正的正样例
precision_score(y_train_5,y_train_pred)
0.3971606344755061
# 召回率 样本中的正例有多少被预测正确
recall_score(y_train_5,y_train_pred)
0.8360081165836561
# f1_score F1分数可以看作是模型精确率和召回率的一种调和平均
from sklearn.metrics import f1_score
f1_score(y_train_5,y_train_pred)
0.5384980988593155
#精度/召回率权衡
# SGDClassifier 对于每个实例，他都会基于决策函数计算出一个分值，如果该值大于阈值 判为正类 反之为父类
# decision_function 返回每个实例的分数
y_scores=sgd_clf.decision_function([some_digit])
y_scores
array([-316208.02724901])
# 手动设置阈值为0
threshold=0
y_some_digit_pred=(y_scores>threshold)
y_some_digit_pred
array([False])
# cross_val_predict获取训练集中所有实例的分数
y_scores=cross_val_predict(sgd_clf,Xtrain,y_train_5,cv=10,method="decision_function")
# P-R 曲线
from sklearn.metrics import precision_recall_curve
# precision_recall_curve 计算所有可能的阈值的精度和召回率
predictions,recalls,thresholds=precision_recall_curve(y_train_5,y_scores)
# 精度
predictions.size
59635
predictions[:-1].size
59634
# 召回率
recalls.size
59635
# 阈值
thresholds.size
59634
def plot_precision_recall_vs_threshold(predictions,recalls,thresholds):
plt.plot(thresholds,predictions[:-1],"b--",label="Precision")
plt.plot(thresholds,recalls[:-1],"g-",label="Recall")
plot_precision_recall_vs_threshold(predictions,recalls,thresholds)
import numpy as np
# np.argmax 会返回最大值的第一个索引 在这种情况下，他表示第一个True的值
threshold_90_precision=thresholds[np.argmax(predictions>=0.9)]
threshold_90_precision
150261.7279476063
y_train_pred_90=(y_scores>=threshold_90_precision)
precision_score(y_train_5,y_train_pred_90)
0.9002298850574713
recall_score(y_train_5,y_train_pred_90)
0.36118797269876407
#ROC曲线
from sklearn.metrics import roc_curve
fpr,tpr,thresholds = roc_curve(y_train_5,y_scores)
def plot_roc_curve(fpr,tpr,label=None):
plt.plot(fpr,tpr,linewidth=2,label=label)
plt.plot([0,1],[0,1],'k--')
plot_roc_curve(fpr,tpr)
from sklearn.metrics import roc_auc_score
# AUC ROC曲线下的面积.
# 计算ROC AUC的值
roc_auc_score(y_train_5,y_scores)
0.9509513817728653
# 使用随机森林进行分类
from sklearn.ensemble import RandomForestClassifier
forest_clf=RandomForestClassifier(random_state=42)
y_probas_forest = cross_val_predict(forest_clf,Xtrain,y_train_5,cv=10,method="predict_proba")
y_probas_forest
array([[0.9, 0.1],
[0.8, 0.2],
[1. , 0. ],
...,
[1. , 0. ],
[1. , 0. ],
[1. , 0. ]])
y_scores_forest=y_probas_forest[:,1]
y_scores_forest
array([0.1, 0.2, 0. , ..., 0. , 0. , 0. ])
fpr_forest,tpr_forest,thresholds_forest=roc_curve(y_train_5,y_scores_forest)
plt.plot(fpr,tpr,"b:",label="SGD")
plot_roc_curve(fpr_forest,tpr_forest,"Random Forest")
plt.legend(loc="lower right")
roc_auc_score(y_train_5,y_scores_forset)
0.990131230702452